“…(African fan palm) is an important non-timber forest product-providing palm that faces multiple anthropogenic threats to its genetic diversity. …”
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“…Diagnostic performance of state-of-the-art scores, such as HINTS (Head Impulse, gaze-evoked Nystagmus, Test of Skew) and ABCD(2) (Age, Blood, Clinical features, Duration, Diabetes), for the differentiation of vestibular stroke vs. peripheral AVS was compared to various machine-learning approaches: (i) linear logistic regression (LR), (ii) non-linear random forest (RF), (iii) artificial neural network, and (iv) geometric deep learning (Single/MultiGMC). …”
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“…Using both regression trees and Random Survival Forests for MFS outcome, we obtained data-driven prediction rules that show F/B from tumor-stroma interface, but not tumor bulk, and S-ODX both contribute to predicting MFS in this patient cohort. …”
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“…RESULTS: Nineteen xylanolytic bacteria (SXB1-SXB19) were isolated from Simlipal forest soil samples following dilution plate technique using corn cob xylan-enriched nutrient agar medium and screened for their xylanase-producing ability. …”
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“…Mantel-Haenszel risk ratio model was used in the meta-analysis, and the results are described using forest plot with 95% confidence interval. Heterogeneity was assessed using the I(2) value. …”
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“…Scalp EEG recordings from a total of 134 patients with epilepsy are used for training a random forest based classification model. Various time-series based features are used to characterize the EEG signal for the classification task. …”
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“…Heterogeneity between-study was explored by forest plot and inconsistency index (I(2)). The publication bias was checked by a funnel plot and Egger’s test. …”
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“…Predictive models used random forest learning (AI: artificial intelligence) to adjust for predictors, and multiple regression analysis to construct ASHRO scores. …”
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“…However, each passing vehicle caused a high mortality risk, and we found large numbers of larvae run over by cars, especially close to covered, forest-like habitat patches. In contrast, adult females in the same area were most often found glowing in more open rocky and grassy habitats. …”
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“…Support Vector Machine (SVM) and Random Forest (RF) classifiers showed comparable performance to previously published models with a mean balanced accuracy over models generated using 5-fold LOCO-CV inside a 10-fold training scheme of 0.759 ± 0.027 when predicting an external test set. …”
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“…The structure of this dataset was investigated using network analysis applied to the pairwise similarities between reported subjective effects and/or chemical compositions. Random forest classifiers were used to evaluate whether reports of flavours and subjective effects could identify the labelled species cultivar. …”
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“…Heterogeneity of the studies was checked by Forest plot and I-squared statistic. Both inverse-variance fixed-effect and DerSimonian and Laird random-effects methods were applied to estimate the pooled level of HRQoL (for both WHO-QoL-BREF and SF-36) and the effect size of associated factors. …”
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“…Four hundred and eighty standardized radiomic features were extracted per tumor. Random forest models were trained to predict lymph node involvement (LNI), presence of any metastasis, Gleason score ≥ 8, and presence of extracapsular extension (ECE). …”
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“…It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. …”
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“…The conventional models and conventional+GRS (genetic risk score) models were developed with Cox regression, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) classifiers in the training set. …”
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“…We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients’ initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality. …”
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“…A meta-analysis of DHS data of the Sub-Saharan countries was conducted to generate pooled prevalence, and a forest plot was used to present it. A multilevel multivariable logistic regression model was fitted to identify determinants of recommended ANC utilization. …”
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“…Accordingly, we developed clinical and radiological models using the following machine learning classifiers, including naive bayes (NB), linear regression (LR), random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), K-nearest neighbor (KNN), kernel support vector machine (k-SVM), and back propagation neural networks (BPNN). …”
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“…The base classifiers in each layer are decision tree, Gradient boosting classifier, logistics regression, random forest and support vector machines which are trained independently and aggregated based on Vote boosting method. …”
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“…Six machine learning models (logistic regression [LR], random forest [RF], decision tree [DT], eXtreme Gradient Boosting [XGB], Gaussian Naïve Bayes [GNB], and K-Nearest Neighbour [KNN]) were used to predict asymptomatic CAS and compared their predictability in terms of the area under the receiver operating characteristic curve (AUCROC), accuracy (ACC), and F1 score (F1). …”
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